class_docstr: ## Trainer
docstr: ### Train the model
docstr_tok: ["#","#","#","Train","the","model"]
code: def train(self):
      """
      ### Train the model
      """
  
      # Loop for the given number of epochs
      for _ in monit.loop(self.epochs):
          # Iterate over the minibatches
          for i, batch in monit.enum("Train", self.dataloader):
              # Move data to the device
              data, target = batch[0].to(self.device), batch[1].to(
                  self.device
              )
  
              # Set tracker step, as the number of characters trained on
              tracker.add_global_step(data.shape[0] * data.shape[1])
  
              # Set model state to training
              self.model.train()
              # Evaluate the model
              output = self.model(data)
  
              # Calculate loss
              loss = self.loss_func(
                  output.view(-1, output.shape[-1]), target.view(-1)
              )
              # Log the loss
              tracker.add("loss.train", loss)
  
              # Calculate gradients
              loss.backward()
              # Clip gradients
              torch.nn.utils.clip_grad_norm_(
                  self.model.parameters(), max_norm=self.grad_norm_clip
              )
              # Take optimizer step
              self.optimizer.step()
              # Log the model parameters and gradients
              if (i + 1) % 100 == 0:
                  tracker.add("model", self.model)
              # Clear the gradients
              self.optimizer.zero_grad()
  
              # Generate a sample
              if (i + 1) % 100 == 0:
                  self.model.eval()
                  with torch.no_grad():
                      self.sample()
  
              # Save the tracked metrics
              if (i + 1) % 10 == 0:
                  tracker.save()
  
          # Save the model
          experiment.save_checkpoint()
  
imports: import dataclasses
  import torch
  from labml_helpers.module import Module
  from torch import nn
  from torch.utils.data import Dataset, DataLoader
  from labml import experiment, lab, tracker, monit, logger
  from labml.logger import Text
  from labml.utils.download import download_file
  from labml_nn.experiments.nlp_autoregression import transpose_batch
  from labml_nn.optimizers.noam import Noam
  from labml_nn.transformers import Encoder, MultiHeadAttention
  from labml_nn.transformers.feed_forward import FeedForward
  from labml_nn.transformers.models import EmbeddingsWithPositionalEncoding, TransformerLayer
  from labml_nn.transformers.utils import subsequent_mask
label_desc: Use image node_id 3 for calling the Trainer obj's underlying member method code with example usage: obj.train() without return types